Image Enhancement under Data-Dependent Multiplicative Gamma Noise

An edge enhancement filter is proposed for denoising and enhancing images corrupted with data-dependent noise which is observed to follow a Gamma distribution. The filter is equipped with three terms designed to perform three different tasks. The first term is an anisotropic diffusion term which is...

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Main Authors: Jidesh Pacheeripadikkal, Bini Anattu
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Applied Computational Intelligence and Soft Computing
Online Access:http://dx.doi.org/10.1155/2014/981932
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author Jidesh Pacheeripadikkal
Bini Anattu
author_facet Jidesh Pacheeripadikkal
Bini Anattu
author_sort Jidesh Pacheeripadikkal
collection DOAJ
description An edge enhancement filter is proposed for denoising and enhancing images corrupted with data-dependent noise which is observed to follow a Gamma distribution. The filter is equipped with three terms designed to perform three different tasks. The first term is an anisotropic diffusion term which is derived from a locally adaptive p-laplacian functional. The second term is an enhancement term or a shock term which imparts a shock effect at the edge points making them sharp. The third term is a reactive term which is derived based on the maximum a posteriori (MAP) estimator and this term helps the diffusive term to perform a Gamma distributive data-dependent multiplicative noise removal from images. And moreover, this reactive term ensures that deviation of the restored image from the original one is minimum. This proposed filter is compared with the state-of-the-art restoration models proposed for data-dependent multiplicative noise.
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institution Kabale University
issn 1687-9724
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publishDate 2014-01-01
publisher Wiley
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series Applied Computational Intelligence and Soft Computing
spelling doaj-art-bbb4295720444994a933b79e700fd8552025-02-03T05:51:54ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322014-01-01201410.1155/2014/981932981932Image Enhancement under Data-Dependent Multiplicative Gamma NoiseJidesh Pacheeripadikkal0Bini Anattu1Department of Mathematical and Computational Sciences, National Institute of Technology, Karnataka 575025, IndiaDepartment of Electronics and Communications Engineering, National Institute of Technology, Karnataka 575025, IndiaAn edge enhancement filter is proposed for denoising and enhancing images corrupted with data-dependent noise which is observed to follow a Gamma distribution. The filter is equipped with three terms designed to perform three different tasks. The first term is an anisotropic diffusion term which is derived from a locally adaptive p-laplacian functional. The second term is an enhancement term or a shock term which imparts a shock effect at the edge points making them sharp. The third term is a reactive term which is derived based on the maximum a posteriori (MAP) estimator and this term helps the diffusive term to perform a Gamma distributive data-dependent multiplicative noise removal from images. And moreover, this reactive term ensures that deviation of the restored image from the original one is minimum. This proposed filter is compared with the state-of-the-art restoration models proposed for data-dependent multiplicative noise.http://dx.doi.org/10.1155/2014/981932
spellingShingle Jidesh Pacheeripadikkal
Bini Anattu
Image Enhancement under Data-Dependent Multiplicative Gamma Noise
Applied Computational Intelligence and Soft Computing
title Image Enhancement under Data-Dependent Multiplicative Gamma Noise
title_full Image Enhancement under Data-Dependent Multiplicative Gamma Noise
title_fullStr Image Enhancement under Data-Dependent Multiplicative Gamma Noise
title_full_unstemmed Image Enhancement under Data-Dependent Multiplicative Gamma Noise
title_short Image Enhancement under Data-Dependent Multiplicative Gamma Noise
title_sort image enhancement under data dependent multiplicative gamma noise
url http://dx.doi.org/10.1155/2014/981932
work_keys_str_mv AT jideshpacheeripadikkal imageenhancementunderdatadependentmultiplicativegammanoise
AT binianattu imageenhancementunderdatadependentmultiplicativegammanoise